Modern approaches and future prospects in lettuce breeding for controlled environment agriculture

Controlled environment agriculture (CEA) is expanding rapidly as a climate-resilient approach for year-round lettuce production near consumption centers. However, despite its supply chain advantages and high productivity, the economic viability and scalability of CEA-grown lettuce remain constrained by the continued reliance on cultivars developed for variable open-field conditions rather than stable, high-input, and space-limited environments of indoor production systems. This mismatch between existing germplasm and CEA-specific production requirements represents a critical gap that has not been comprehensively addressed in lettuce breeding. This review discusses the need for a paradigm shift in breeding priorities toward cultivars specifically optimized for CEA, with an emphasis on traits that improve output per unit of energy, time, and growing area. Key breeding targets include accelerated growth and shortened crop cycles, enhanced light-use efficiency and photosynthetic performance, improved tolerance to temperature extremes, compact plant architecture for high-density production, optimized shoot-to-root biomass partitioning for hydroponic systems, and resilience to major CEA-associated physiological disorders, pests, and diseases. Because CEA reduces many field-related stressors, breeding efforts can place greater emphasis on consumer- and market-oriented traits, including consistent product quality, enhanced nutritional value, and uniformity compatible with automated production systems. This review consolidates dispersed evidence on these CEA-targeted traits and links them to a modern lettuce breeding toolbox, providing a framework to guide cultivar development for controlled environments. We outline how integrating approaches ranging from conventional genetics and marker-assisted selection to genomic selection and gene-editing technologies such as CRISPR can accelerate genetic gain for these complex trait combinations. Furthermore, the integration of high-throughput phenotyping, artificial intelligence, and multi-omics platforms offers a powerful, data-driven approach to decipher genotype-by-environment interactions and optimize lettuce for controlled environments. By identifying critical knowledge gaps and outlining a targeted breeding framework, this review provides a roadmap for developing resource-efficient, high-performing, quality-focused lettuce cultivars tailored to next-generation CEA, thereby improving the industry's profitability, sustainability and ability to meet the food demands of a rapidly urbanizing global population.